Conditional random field tracking model based on a visual long short term memory network

In dense pedestrian tracking, frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories. In this study, a conditional random field tracking model is established by using a visual long short term memory network in the three dimensional space and the motion estimations jointly performed on object trajectory segments. Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation. To address the uncertainty of the length and interval of trajectory segments, a multimode long short term memory network is proposed for the object motion estimation. The tracking performance is evaluated using the PETS2009 dataset. The experimental results show that the proposed method achieves better performance than the tracking methods based on independent motion estimation.